Computer-implemented system and method for determining attentiveness of user

ABSTRACT

Disclosed herein is a method and system for collecting attentiveness information associated with a user&#39;s response to consuming a piece of media content. The attentiveness information is used to create an attentiveness-labelled behavioural data for the user&#39;s response. A computer-implemented attentiveness model may be generated by applying machine learning techniques to the a set of attentiveness-labelled behavioural data from multiple users. The system may comprise an annotation tool that facilitates human labelling of the user&#39;s response with attentiveness data. The resulting attentiveness model is therefore based on correlations indicative of attentiveness within the attentiveness-labelled behavioural data and/or physiological data that are based on real human cognition rather than a predetermined feature or combination of features.

CROSS REFERENCE TO RELATED APPLICATIONS

This patent application claims the benefit of UK patent application no.1809388.0 filed on 7 Jun. 2018, which is incorporated by referenceherein.

FIELD OF THE INVENTION

The invention relates to a computer-implemented system and method ofdetermining user reaction to media content. In particular, the inventionrelates to a computer-implemented tool for obtaining and utilising dataindicative of a user's attentiveness whilst consuming media content.

BACKGROUND TO THE INVENTION

Certain types of media content, such as advertising, music videos,movies, etc., aim to induce changes in a consumer's emotional state,e.g. to catch a user's attention or otherwise increase theirattentiveness. In the case of advertising, it may be desirable totranslate this change in emotional state into performance, such as saleslift. For example, a television commercial may look to increase sales ofa product to which it relates. There is demand for being able toevaluate the effectiveness of media content prior to publication.

Active feedback, which is also referred to as self-reported feedback, issometimes used in attempts to determine or predict the performance ofpieces of media content, such as video commercials. For active userfeedback, users provide verbal or written feedback after consuming apiece of media content. For example, the users may complete aquestionnaire, or may provide spoken feedback that can be recorded foranalysis, e.g. manually or in an automated manner using speechrecognition tools. Feedback may include an indication of emotional stateexperienced while consuming the piece of media content. However, activefeedback from users pulls from rationalised, conscious thoughtprocesses, rather than the (passive) emotional state actuallyexperienced. It has been shown that user preferences are outside ofconscious awareness, and strongly influenced by passive emotional state.Media content performance therefore cannot be accurately predicted usingactive emotional state feedback.

It is known that emotional state data can also be measured in a passivemanner, e.g. by collecting data indicative of a user's behavioural orphysiological characteristics, e.g. while the user consumes a piece ofmedia. In one example, facial responses can be used as passiveindicators of experienced emotional state. Webcam video acquisition canbe used to monitor facial responses, by capturing image frames as apiece of media content is consumed by a user. Emotional state cantherefore be captured through the use of webcams, by processing videoimages.

Physiological parameters can also be good indicators of experiencedemotional state. Many physiological parameters are not consciouslycontrollable, i.e. a consumer has no influence over them. They cantherefore be used to determine the true emotional state of a userconsuming a piece of media content, which can in principle be used toaccurately predict media content performance. Examples of physiologicalparameters that can be measured include voice analysis, heartrate,heartrate variability, electrodermal activity (which may be indicativeof arousal), breathing, body temperature, electrocardiogram (ECG)signals, and electroencephalogram (EEG) signals.

It is increasingly common for users to posses wearable or portabledevices capable of recording physiological parameters of the typedescribed above. This opens up the possibility that such physiologicalmeasurements may be scalable to large sample sizes, which may enablestatistical variations (noise) to be removed so that correlation withmedia content performance can be seen.

Emotional state information measured in this way has been shown tocorrelate with media content performance, and in particular sales lift.The proliferation of webcams on client devices means that capture ofthis type of data can be scaled to large sample sizes.

The behavioural characteristics of a user may manifest themselves in avariety of ways. References to “behavioural data” or “behaviouralinformation” herein may refer to visual aspects of a user's response.For example, behavioural information may include facial response, headand body gestures or pose, and gaze tracking. In practice, it can bedesirable to use a combination of raw data inputs comprising behaviouraldata, physiological data and self-reported data in order to obtainemotional state information. A combination of raw data from two or threeof the sources mentioned above may be useful in identifying “false”indicators. For example, if emotional state data derived from all threesources overlaps or is aligned, it gives more confidence in the obtainedsignal. Any inconsistency in the signal may be indicative of a falsereading.

Furthermore, some types of data may indicate only presence or absence ofan emotion or attentiveness, but not the opposite. For example, aresponse with a variety of facial expressions may indicate high levelsof attentiveness. However, the absence of changing facial expressionsdoes not mean low levels of attentiveness. Similarly, a constantlychanging head pose may indicate low levels of attentiveness, but a fixedhead pose does not necessarily mean high attentiveness.

False indications may arise where behavioural characteristics arerecorded for a user who is reacting to something other than the mediacontent currently on display.

For example, the user may be distracted by another person while themedia content is displayed. In that situation the behaviouralcharacteristics of the user may be primarily influenced by theirconversation with the other person, and therefore do not accuratelyreflect the user's response to the media content. The user'sattentiveness to or engagement with the media content is therefore animportant factor in determining the relevance of their collectedbehavioural characteristics. Moreover, attentiveness is recognized as anantecedent or gatekeeper to other mental processes. In the world ofadvertising, to be successfully ads must attract sufficient attention tobe able to impact viewers and their memory of the ad/brand/product.

SUMMARY OF THE INVENTION

At its most general, the present invention proposes a system forcollecting attentiveness information associated with a user's responseto consuming a piece of media content. The attentiveness information maybe used to create an attentiveness-labelled behavioural data for theuser's response. A computer-implemented attentiveness model may begenerated by applying machine learning techniques to the a set ofattentiveness-labelled behavioural data from multiple users.

The system for collecting attentiveness information may comprise anannotation tool that facilitates manual (i.e. human-driven) labelling ofthe user's response with attentiveness data. The resulting attentivenessmodel may thus be based on correlations indicative of attentivenesswithin the attentiveness-labelled behavioural data and/or physiologicaldata that are based on real human cognition rather than a predeterminedfeature or combination of features. It is possible to predict thatcertain behavioural characteristics (e.g. blink rate, head pose changes,gaze direction changes, facial expressions) will strongly correlate withattentiveness. In principle such characteristics may be used as a proxyfor attentiveness. However, this approach can miss the context in whichthese characteristics occur, which can in turn increase the risk offalse indications. By relying instead on source data that reportsdirectly on attentiveness, the attentiveness model may avoid suchproblems whilst also being sensitive to more subtle correlations.

The system disclosed herein may have two aspects. In a first aspect,there is provided a data collection system that includes an annotationtool. The output from that system may be attentiveness-labelled responsedata, as discussed below. In a second aspect, there is provided a dataanalysis system that can operate using an attentiveness model obtainedusing the attentiveness-labelled response data in order to outputinformation indicative of attentiveness without requiring human input.The invention may provide a computer-implemented method corresponding toeach of these aspects.

Thus, in one aspect, the invention provides a computer-implementedmethod of determining user attentiveness during media contentconsumption, the method comprising: obtaining, at a collection server,response data (e.g. behavioural and/or physiological data) from a clientdevice, wherein the response data is collected for a user consumingmedia content on the client device, and wherein the response datacomprises a data stream representative of variation over time of theuser's behaviour whilst consuming the media content; associating, at thecollection server, the data stream to the media content; displaying, atan annotation device, a dynamic representation of the response dataconcurrently with the media content to which it is associated;receiving, at the annotation device, label data indicative of userattentiveness; and generating attentiveness-labelled response data inwhich the label data is associated with events in the data stream ormedia content.

A piece of media content may be consumable by a plurality of users, eachof the plurality of users being at a respective client device. Themethod may comprise collecting, at each of a plurality of the respectiveclient devices, raw input data indicative of a plurality of userresponses to the piece of media content. In this manner,attentiveness-labelled response data can be obtained from a range ofusers, which may make it suitable for use as a training set for anattentiveness model.

The method may be usefully implemented in a networked environment, e.g.to allow for efficient use of resources. For example, each client devicemay be communicable with a server device over a network, wherein theprocessing of the collected raw input data can occur either locally orat the server device.

The term “data stream” may be used herein to indicate data that isindicative of the time evolution of one or more parameters or otherresponse features. In other words, the data stream may comprisetime-varying information. The parameters may be extracted from theinformation collected at the client device. This extraction may be doneby the client device or some separate processing unit. In one example,the collected information comprises a video or sequence of image framescaptured of the user. The collected information may be particularlyuseful where the video or sequence of image frames comprises facialimage data of the user during consumption of the media content. A user'sfacial movement in response to media content may be a key indicator ofattentiveness used by an annotator applying the labels. In anotherexample the collected information may be physiological data measured atthe user. Examples of physiological parameters that can be measuredinclude voice analysis, heartrate, heartrate variability, electrodermalactivity (which may be indicative of arousal), breathing, bodytemperature, electrocardiogram (ECG) signals, and electroencephalogram(EEG) signals.

The step of associating the data stream may comprise synchronising orotherwise mapping the collected data (e.g. visible data and/orphysiological data) with or to the media content. This can be done inany known manner, e.g. by matching timing information between playbackof the media content and the collected data. In one example, theresponse data may be time stamped, and the step of synchronising thecollected data with the media content may comprise matching the timestamp data with media playback status data. The media playback statusinformation may be transmitted from the client device, e.g. based oninformation from a media player which played back the media content.

The data stream may comprise a plurality of different types of responsedata. By providing different types of data, the data stream mayfacilitate annotation of attentiveness by providing the annotation witha rich picture of many aspects of the user's reaction. Moreover,following labelling for attentiveness, the different type of data maycontinue to provide additional response parameters and therefore providea richer training set for a machine learning process to established anattentiveness model.

The data stream may comprise any of: emotional state data, media controldata, media tag data, and media playback status data. Emotional statedata may be derived or determined from captured facial images and/orphysiological data using known techniques, or may be provided by theuser, e.g. in the form or verbal or written feedback. Media control datamay comprise information from the media player on which the mediacontent is played back, e.g. relating to how and at what point the userinteracted with the media content. For example, the media control datamay include pause/resume, stop, rewind, fast forward, volume control,etc. The media tag data may be time stamped or otherwise variableinformation that relates to the subject matter of the media content atany given time. Herein “subject matter” may mean information about whatis visible in an image or video, or an indicative of a relevantnarrative arc, or the like. For example, it may include data indicativeof a sentiment of the media content or the media content's audio track.The data stream may also include data indicative of a saliency ofattentiveness associated with the media content. The saliency ofattentiveness may be a parameter, e.g. a time-varying parameter, thatrelates to the likelihood of media content being attention-grabbing. Forexample, a portion of media content with rapidly changing scenes ordramatic audio may have a higher saliency than a more static portion.This information may be useful for providing contextual information inthe attentiveness-labelled response data.

As with any of the parameters in the data stream, the media tag data maybe used to filter the attentiveness-labelled response data, e.g. toprovide a training set that is relevant for a particular subset of usersor a particular type of media content. The media playback status datamay comprise information about the quality and other relevantcircumstances of how the media content was played back at the clientdevice. For example, the media playback status may indicate whenunexpected pauses or delays occur in playback of the media content, e.g.due to buffering or network problems. The media playback statusinformation may be collected and supplied by the client device.

The step of generating the attentiveness-labelled response data maycomprise adding an attentiveness label parameter to the data stream. Inother words, the label data that is applied may be consolidated orotherwise processed to create a data time series for an attentivenesslabel parameter, which can be synchronised or aligned with the otherparameters in the data stream. The label data and/or attentiveness labelparameter may be a score of attentiveness, e.g. some measure thatenables comparison, such as a numeric or other value-based identifier,or variation within an unbounded range, where the comparison is based onrelative change rather than absolute values. In one example, the labeldata may be a plurality of preset levels, which may be numeric (e.g. 1,2, 3) or have suitable identifiers (e.g. high, medium, low). There maybe any number of levels, e.g. 5 or more, or 10 or more. In anotherexample, the label data may be assigned from a sliding scale, such as alinear numeric scale (e.g. from 0 to 100). It may be understood that theinvention need not be limited to any specific form of scoring rules.

In one example, attentiveness data may be obtained from multipleannotators and aggregated or otherwise combined to yield anattentiveness score for a given response. For example, attentivenessdata from multiple annotators may be averaged over portions of the mediacontent.

In one embodiment, the level of agreement between multiple annotatorsmay itself be used as way of quantifying attentiveness. This may allowrich data to be obtained even if the annotation task itself is simply,e.g. a binary option of either (a) attentive, or (b) not attentive. Insome circumstances a third option (c) unknown, may be included tofacilitate annotation of portions where it is not possible to judgelevel of attentiveness based on data available.

The method may therefore include receiving attentiveness data frommultiple annotators, and generating combined attentiveness data from thedifferent sets of attentiveness data. The combined attentiveness datamay comprise an attentiveness parameter that is indicative of level ofpositive correlation between the attentiveness data from the pluralityof annotators. The attentiveness parameter may be a time-varyingparameter, i.e. the score indicating agreement may vary across theduration of the response data to indicate increasing or decreasingcorrelation.

Each annotator may have a confidence value associated therewith. Theconfidence value may be calculated based on how well that annotator'sindividual scores correlate with the combined attentiveness data. Theconfidence values may be updated dynamically, e.g. as more data isreceived from each individual annotator. The confidence values may beused to weight the attentiveness data from each annotator in the processof generating the combined attentiveness data.

Where the behavioural data comprises emotional state data, the methodmay further comprise deriving a significance score or weighting for theemotional state data based on the received label data indicative of userattentiveness. In other words, the information about a user'sattentiveness is used to affect the influence of the emotional statedata for the user in future steps. Thus, if it were useful to try todetermine how a piece of media content made people feel, the inventionwould be able to reduce the impact of emotion reactions from people whowere not engaged with the media content. This may be beneficial becauseotherwise the emotional reactions from non-engaged users could skew thefindings.

In addition to the information about the time evolution of response dataparameters in the data stream, the attentiveness-labelled response datamay comprise static user data. For example, the static information maybe profile information about the user, e.g. gender, age, location, orany other demographic detail. The static user data may be used as aparameter in the neural network or as a filter so that attentiveness fora certain class of users can be assessed and/or weighted independently.

In practice, the application of the label data may require the responsedata and media content to be considered repeatedly. To assist in this,the method may include controlling the concurrently displayed dynamicrepresentation of the response data and the media content to which it isassociated. Controlling here may mean that the concurrently displayedmaterial can be manipulated as one, e.g. by pausing, rewinding,fast-forwarding, frame stepping, or any other technique. The displayedresponse data may comprise any parameter mentioned with respect to thedata stream above. The display data may be visual data such as facialimages, and/or may be graphical representations of non-visible data,such as physiological data.

A display that combines multiple behavioural parameters may furtherimprove the quality and speed of human annotations. It assistsannotators in identifying areas in the timeline where changes in theattentiveness are likely to have happened and they can assess whetherthose changes are due to the media content or induced by other factors.

One important use of the attentiveness-labelled response data is ingenerating an attentiveness model capable of scoring user attentivenessbased on collected data without human input. The method may thus furthercomprise: storing, in a data repository, attentiveness-labelled responsedata from multiple users; extracting, from the data repository by ananalysis server, an attentiveness-labelled response data training set;establishing, at the analysis server, an objective for a machinelearning algorithm; and generating, using the machine learningalgorithm, an attentiveness model from the attentiveness-labelledresponse data training set. Any suitable machine learning process may beused, although an artificial neural network may be preferred.

In another example, rather than obtaining human annotations, theattentive model may instead use one or a subset of parameters in thedata stream as a ground truth against which the attentiveness model canbe trained. For example, where physiological data is recorded, this maybe used as a target for an artificial neural network. In this example,the attentiveness model can effectively predict a physiological response(which may be indicate of attentiveness) based on collected informationfor users where physiological data is not available. In another example,attentiveness saliency may be used as a target for the attentivenessmodel.

When both physiological and human-label data are present, the two typesof data can be merged for a combined overall improved measure ofattentiveness, which itself can then used as a target for artificialneural network.

The method may comprise obtaining, at the collection server, newresponse data (e.g. behavioural data and/or physiological data) from aclient device, wherein the response data is collected for another userconsuming media content on the client device, and wherein the responsedata comprises a data stream representative of variation over time ofthe user's behaviour whilst consuming the media content; and inputtingthe new response data to the attentiveness model to obtain anattentiveness score for one or more portions of the new response data.The attentiveness model may itself divide the response into portionshaving different durations, e.g. corresponding to a frame or a certainsequence of frames in the media content or behavioural data (especiallythe facial image data). The new behavioural data may comprise facialimage data of the other user during consumption of the media content.

The attentiveness model may be targeted or tailored. For example, thestep of extracting the attentiveness-labelled response data training setmay comprise applying a filter to the attentiveness-labelled responsedata in the repository. The filter may be based on user type, mediatype, media content, quality/reliability of a label, intensity ofattentiveness, etc. The resulting attentiveness model may thus beappropriate for certain circumstance. A plurality of attentivenessmodels adapted for certain scenarios may be obtained from the same datarepository.

As mentioned above, the attentiveness-labelled response data in thetraining set may comprise media tag data indicative of subject matter inthe media content being consumed. The method may further comprise:obtaining new media tag data for another piece of media content, andinputting the new media tag data to the attentiveness model to obtain anattractiveness score for one or more portions of the piece of mediacontent. In other words, the attentiveness model may have due regard tothe subject matter, structure or layout of the media content. It maythus be able to predict how people will be engaged with the mediacontent based on its subject matter and presentation. A meaningfulattentiveness score may be very useful in preventing unnecessaryhuman-based testing of the media content before launch. This may beuseful in an example where attentiveness is expected at particularportions of the media, e.g. brand reveal in an advertisement, on inaction-packed sequences in other media. Taking this information intoaccount may be a way of obtaining training data more efficiently, or itmay enable the attentiveness model to be targeted at particular types ofevent that occur in a given piece of media (e.g. brand reveal inadvertising). As discussed above, this kind of attentiveness saliencyinformation may be part of the data stream that is used to facilitateannotation, i.e. mixed with other measures of attentiveness, such as thehuman-labelled data and/or physiological data. A mixed data stream suchas this may represent better attentiveness data for training anattentiveness model. For example, attentiveness saliency deduced fromthe content itself can be used to weight the human labels.

Network-based computing systems may be configured to execute the methodsteps outlined above. For example, in another aspect of the inventionthere is provided a system for determining user attentiveness duringmedia content consumption, the system comprising: a collection servercommunicatively coupled via a network to a plurality of client devices,the collection server being configured to: obtain response data (e.g.behavioural data and/or physiological data) from the plurality of clientdevices, wherein the response data is collected for a user consumingmedia content on the client device, and wherein the response datacomprises a data stream representative of variation over time of theuser's behaviour whilst consuming the media content; and map the datastream to the media content; and an annotation device communicativelycoupled to the collection server, the annotation device being configuredto: display a dynamic representation of the response data concurrentlywith the media content to which it is associated; receive label dataindicative of user attentiveness; and generate attentiveness-labelledresponse data in which the label data is associated with events in thedata stream or media content. The annotation device may be a computerterminal displaying a graphical user interface that provides therelevant functionality. This system may be arranged to carry out any ofthe method steps discussed above.

In another aspect, the disclosure herein may provide a system fordetermining user attentiveness during media content consumption, thesystem comprising: a data repository storing attentiveness-labelledresponse data from multiple users, the attentiveness-labelled responsedata comprising: a data stream representative of variation over time ofa user's behaviour whilst consuming a piece of media content, and labeldata indicative of user attentiveness associated with events in the datastream or media content; and an analysis server configured to: extractfrom the data repository an attentiveness-labelled response datatraining set; and generate, using a machine learning algorithm, anattentiveness model from the attentiveness-labelled response datatraining set, receive new response data, and apply the attentivenessmodel to the new response data to obtain an attentiveness score for oneor more portions of the new response data. This aspect provides a systemthat can obtain the attentiveness model and apply it to new responsedata.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention are discussed in detail below withreference to the accompanying drawings, in which:

FIG. 1 is a schematic diagram of a data collection and analysis systemthat is an embodiment of the invention;

FIG. 2 is a flow diagram of a data collection method that is anembodiment of the invention;

FIG. 3 is a schematic diagram showing a data structure forattentiveness-labelled response data that may be used in embodiments ofthe invention;

FIG. 4 is a flow diagram of a data analysis method that is an embodimentof the invention;

FIG. 5 is a schematic screen shot of an example annotation tool that maybe used in embodiments of the invention; and

FIG. 6 is a flow diagram of a data analysis method that is anotherembodiment of the invention.

DETAILED DESCRIPTION; FURTHER OPTIONS AND PREFERENCES

Embodiments of the invention relate to a system and method of collectingand utilising behavioural data from a user while the user is consuming apiece of media content. FIG. 1 is a schematic diagram of a complete datacollection and analysis system 100 that is an embodiment of theinvention. It can be understood that the system in FIG. 1 illustratescomponents for performing collection and annotation of data, as well asfor subsequent use of that data in generating and utilising anattentiveness model. In other examples separate systems having thesefunctionalities may be provided.

The system 100 is provided in a networked computing environment, where anumber of processing entities are communicably connected over one ormore networks. In this example, the system 100 comprises one or moreclient devices 102 that arranged to playback media content, e.g. viaspeakers or headphones and a display 104. The clients devices 102 mayalso comprise or be connected to behavioural data capture apparatus,such as webcams 106, microphones, etc. Example client devices 102include smartphones, tablet computers, laptop computers, desktopcomputers, etc.

The system 100 may also comprise one of more client sensors units, suchas a wearable device 105 for collecting physiological information from auser while they consume media content on a client device 102. Examplesof physiological parameters that can be measured include voice analysis,heartrate, heartrate variability, electrodermal activity (which may beindicative of arousal), breathing, body temperature, electrocardiogram(ECG) signals, and electroencephalogram (EEG) signals.

The client devices 102 are communicably connected over a network 108,such that they may receive media content 112 to be consumed, e.g. from acontent provider server 110.

The client devices 102 may further be arranged to send collectedbehavioural information over the network for analysis or furtherprocessing at a remote device, such as analysis server 118. As mentionedabove, references to “behavioral data” or “behavioral information”herein may refer to any collected information about the user's response,e.g. visual aspects of a user's response or physiological data. Forexample, behavioral information may include facial response, head andbody gestures or pose, and gaze tracking.

In this example, the information sent to the analysis server 118 mayinclude a user's facial response 116, e.g. in the form or a video or setof images captured of the user while consuming the media content. Theinformation may also include the associated media content 115 or a linkor other identifier that enables the analysis server 118 to access themedia content 112 that was consumed by the user. The associated mediacontent 115 may include information concerned the manner in which themedia content was played back at the client device 102. For example, theassociated media content 115 may include information relating to userinstructions, such a pause/resume, stop, volume control, etc.Additionally or alternatively, the associated media content 115 mayinclude other information about delays or disruptions in the playback,e.g. due to buffering or the like. The analysis server 118 may thuseffectively receive a data stream comprises information relating to auser's response to the piece of media content.

The information sent to the analysis server 118 may also includephysiological data 114 obtained for the user while consuming the mediacontent. The physiological data 114 may be transmitted directly by thewearable device 105, or the wearable device 105 may be paired with oneor more client devices 102, which are arranged to receive and send ondata from the wearable device 105. The client device 102 may be arrangedto process raw data from the wearable device, whereby the physiologicaldata 114 transmitted to the analysis server 118 may comprise dataalready processed by the client device 102.

In the present example, the purpose of collecting information about theuser's response to the media content is to enable that response to beannotated with attentiveness labels. In one example, this annotationprocess may comprise establishing a time series of attentiveness scoresthat map onto a time series of one or more behavioural characteristicparameters received at the analysis server 118. For example, the timeseries of attentiveness scores may be associated with the images orvideo of the user collected while the user was consuming the mediacontent. Other behavioural characteristic parameters, e.g. emotionalstate information, physiological information, etc., may be synchronisedwith the images or video of the user. An output of the annotationprocess may thus be a rich data stream representative of the user'sbehavioural characteristics, including attentiveness, in response to themedia content.

The system 100 provides an annotation tool 120 that facilitatesexecution of the annotation process. The annotation tool 120 maycomprise a computer terminal in communication (e.g. networkedcommunication) with the analysis server 118. The annotation tool 120includes a display 122 for showing a graphical user interface to a humanannotator (not shown). The graphical user interface may take many forms.However, in may usefully comprise a number of functional elements.Firstly, the graphical user interface may present collected userresponse data 116 (e.g. the set of facial images or video showing theuser's facial movements) alongside associated media content 115 in asynchronised manner. In other words, the user's facial reactions aredisplayed simultaneously with the associated media content that theconsumer was watching. The graphical user interface may also present asuitable graphical representation of the physiological data 114.Alternatively or additionally, the graphical user interface may alsopresent a graphical representation of attentiveness saliency 117associated with the media content. In this manner the annotator can beaware (consciously or subconsciously) of the context in which the user'sresponse occurred. In particular, the annotator may be able to adjudgeattentiveness based on a reaction to events in the associated mediacontent, or may be sensitive to external events that may have distractedthe user.

The graphical user interface may include a controller 124 forcontrolling playback of the synchronised response data 116 andassociated media content. For example, the controller 124 may allow theannotator to play, pause, stop, rewind, fast forward, backstep, forwardstep, scroll back, scroll forward or the like through the displayedmaterial.

The graphical user interface may include one or more score applicators126 for applying an attentiveness score to a portion or portions of theresponse data 116. In one example, a score applicator 126 may be used toapply an attentiveness score to a period of a video or set of imageframes corresponding to a given time period of the user's response. Theattentiveness score may have any suitable format. In one example it isbinary, i.e. a simple yes/no indication of attentiveness. In otherexamples, the attentiveness score may be selected from a set number ofpredetermined levels (e.g. high, medium, low), or may be chosen from anumerical range (e.g. a linear scale) between end limits that representno attention (or absence) and high attention respectively.

Simplifying the annotation tool may be desirable in terms of expandingthe potential annotator pool. The simpler the annotation process, theless training is required for annotators to participate. In one example,annotated data may be harvested using a crowd-sourcing approach.

The annotation tool 120 may thus represent a device for receiving a timeseries of data indicative of a user's attentiveness while consuming apiece of media contact. The attentiveness data may be synchronised (e.g.by virtue of the manner in which the score is applied) with the responsedata 116. The analysis server 118 may be arranged to collate orotherwise combine the received data to generate attentiveness-labelledresponse data 130 that can be stored in a suitable storage device 128.

The attentiveness data from multiple annotators may be aggregated orotherwise combined to yield an attentiveness score for a given response.For example, attentiveness data from multiple annotators may be averagedover portions of the media content.

In one embodiment, the level of agreement between multiple annotatorsmay itself be used as way of quantifying attentiveness. For example, theannotation tool 120 may permit each annotator with a binary option toscore the response data: the user is either (a) attentive, or (b) notattentive. The annotator tool 120 may present one or more reasons fieldsin which an annotator can provide a reason for the binary selection.There may be a drop down list or the like of predetermined reasons fromwhich field may be populated. The predetermined reasons may includecommon reasons for attention or inattention, e.g. “turning head away”,“not looking at screen”, “talking”, etc. The field may also permit freetext entry. The attentiveness data from each annotator may include theresults of the binary selection for various periods within the responsedata, together with associated reasons. The reasons may be used toassess circumstances in which there is a high degree of disagreementbetween annotators, or where an attentiveness model outputs a resultthat does not agree with observation. This can happen, for example,where similar facial movements correspond to different behaviours (e.g.talking/eating, etc.).

The analysis server 118 may be arranged to receive the attentivenessdata from multiple annotators. The analysis server 118 may generatecombined attentiveness data from the different sets of attentivenessdata. The combined attentiveness data may comprise an attentivenessparameter that is indicative of level of positive correlation betweenthe attentiveness data from the plurality of annotators. In other words,the analysis server 118 may output a score that quantifies the level ofagreement between the binary selections made by the plurality ofannotators across the response data. The attentiveness parameter may bea time-varying parameter, i.e. the score indicating agreement may varyacross the duration of the response data to indicate increasing ordecreasing correlation.

In a development of this concept, the analysis server 118 may arrangedto determine and store a confidence value associated with eachannotator. The confidence value may be calculated based on how well theannotators individual scores correlate with the combined attentivenessdata. For example, an annotator who regularly scores in the oppositedirection to the annotator group when taken as a whole may be assigned alower confidence value than an annotator who is more often in line. Theconfidence values may be updated dynamically, e.g. as more data isreceived from each individual annotator. The confidence values may beused to weight the attentiveness data from each annotator in the processof generating the combined attentiveness data. The analysis server 118may thus exhibit the ability to ‘tune’ itself to more accurate scoring.

The attentiveness-labelled response data 130 may include theattentiveness parameter. In other words, the attentiveness parameter maybe associated with, e.g. synchronised or otherwise mapped to or linkedwith, events in the data stream or media content.

The attentiveness-labelled response data 130 may include any one or moreof: the original collected data 116 from the client device 102 (e.g. theraw video or image data, which is also referred to herein as theresponse data); the time series of attentiveness data; time series datacorresponding to one or more physiological parameters from thephysiological data 114; and emotional state data extracted from thecollected data 116.

The collected data may be image data captured at each of the clientdevice 102. The image data may include a plurality of image framesshowing facial images of a user. Moreover, the image data may include atime series of image frames showing facial images of a user.

Where the image frames depict facial features, e.g. mouth, eyes,eyebrows etc. of a user, and each facial feature comprises a pluralityof facial landmarks, the response data may include informationindicative of position, shape, orientation, shading etc. of the faciallandmarks for each image frame.

The image data may be processed on respective client devices 102, or maybe streamed to the analysis server 118 over the network 108 forprocessing.

The facial features may provide descriptor data points indicative ofposition, shape, orientation, sharing, etc., of a selected plurality ofthe facial landmarks. Each facial feature descriptor data point mayencode information that is indicative of a plurality of faciallandmarks. Each facial feature descriptor data point may be associatedwith a respective frame, e.g. a respective image frame from the timeseries of image frames. Each facial feature descriptor data point may bea multi-dimensional data point, each component of the multi-dimensionaldata point being indicative of a respective facial landmark.

The emotional state information may be obtained directly from the rawdata input, from the extracted descriptor data points or from acombination of the two. For example, the plurality of facial landmarksmay be selected to include information capable of characterizing useremotion. In one example, the emotional state data may be determined byapplying a classifier to one or more facial feature descriptor datapoints in one image or across a series of images. In some examples, deeplearning techniques can be utilised to yield emotional state data fromthe raw data input.

The user emotional state may include one or more emotional statesselected from anger, disgust, fear, happiness, sadness, and surprise.

The creation of the attentiveness-labelled response data represents afirst function of the system 100. A second function, described below, isin the subsequent use of that data to generate and utilise anattentiveness model.

The system 100 may comprise a modelling server 132 in communication withthe storage device 128 and arranged to access the attentiveness-labelledresponse data 130. The modelling server 132 may connect directly to thestorage device 128 as shown in FIG. 1 or via a network such as network108.

The modelling server 132 is arranged to apply machine learningtechniques to a training set of attentiveness-labelled response data 130in order to establish a model 136 for scoring attentiveness fromunlabelled response data, e.g. response data 116 as originally receivedby the analysis server 118. The model may be established as anartificial neural network trained to recognise patterns in collectedresponse data that are indicative of high levels of attentiveness. Themodel can therefore be used to automatically score collected responsedata, without human input, for attentiveness. An advantage of thistechnique is that the model is fundamentally based on directmeasurements of attentiveness that are sensitive to contextual factorsthat may be missed by measurements or engagement or attentiveness thatrely on certain predetermined proxies.

In one example, the attentiveness-labelled response data used togenerate the attentiveness model may also include information about themedia content. This information may relate to how the media content ismanipulated by the user, e.g. paused or otherwise controlled.Additionally or alternatively, the information may include data aboutthe subject matter of the media content on display, e.g. to give contextto the collected response data.

Herein the piece of media content may be any type of user-consumablecontent for which information regarding user feedback is desirable. Theinvention may be particular useful where the media content is acommercial (e.g. video commercial or advert), where user engagement orattention is likely to be closely linked to performance, e.g. salesuplift or the like. However, the invention is applicable to any kind ofcontent, e.g. any of a video commercial, an audio commercial, a movietrailer, a movie, a web advertisement, an animated game, an image, etc.

FIG. 2 is a flow diagram of a data collection method 200 that is anembodiment of the invention. The method commences when a user initiatesplayback of a piece of media content on a client device. The methodincludes a step 202 of obtaining response data from the user while theyconsume the media content. As explained above, the response data may becollected from a range of device, e.g. a webcam recording facial images,a physiological sensor (e.g. in a wearable device) recordingphysiological data, a microphone recording audio, etc. The response datamay be collected and combined by a client device, and then transmittedto an analysis device for further processing.

The method continues with a step 204 of mapping or synchronising theresponse data with the media content that was consumed. This may be doneby the client device or analysis server, e.g. by aligning time stamps onthe collected response data with known information about the playbacktime of the media content.

The method continues with a step 206 of concurrently displayinginformation indicative of the response data with the media content. Inone example this may mean simultaneously displaying the recorded imagesof the user alongside the media content.

The method continues with a step 208 of receive annotations indicativeof the level of attentiveness of the user to the media content. Theannotations may be supplied by a human annotator who watches theconcurrently displayed response data and media content and makes a judgeabout the extent to which the user is engaged with the media content.

The method continues with a step 210 of generatingattentiveness-labelled response data in which the annotations indicativeof attentiveness are incorporated e.g. as a separate on integrated datatime series, with the response data for subsequent use. In one example,emotional state information may also be extracted from the collectedresponse data. The attentiveness-labelled response data may thus includeany or all of raw collected data, emotional state data derivedtherefrom, collected physiological data, attentiveness data, and datarelating to the media content. The raw collected data may compriseimages of the user together with other user data, e.g. demographic data,geographic data, or the like.

FIG. 3 is a schematic diagram showing a data structure 300 forattentiveness-labelled response data that may be used in embodiments ofthe invention. The data structure may comprise a set of time varyingparameters 302, and a set of static data 304. In this example, the timevarying parameters include emotional state data 306, media control data308, attentiveness label data 310, physiological data 311, attentivenesssaliency 313, and media tag data 312. The set of static data 304comprises user data 314.

The media control data 308 may indicate how the user interacts with amedia player on which the media content was delivered, e.g. by pausingor otherwise altering the playback conditions. The media tag data 312may represent a series of tags that are indicative of the subject matterof the media content from time to time. In the case of video content,the tags are likely to vary between scenes in the video, and maytherefore represent a high level abstraction of the content that maynevertheless correlate with user attention.

FIG. 4 is a flow diagram of a data analysis method 400 that utilises theattentiveness-labelled response data discussed above. The method beginswith a step 402 of obtaining the attentiveness-labelled response datafrom a plurality of users. This may be done by the analysis serverdiscussed above, which can stored attentiveness-labelled response datafor multiple users in the storage device.

The method continues with a step 404 of establishing, from the obtainedattentiveness-labelled response data, a training set and one or moreobjectives for an artificial neural network that is configured to supplyan attentiveness score from collected data (in particular images of auser's reaction to media content).

Using the training set, the method continues with a step 406 ofgenerating an attentiveness model. The attentiveness model may be usedto score portions of collected data for attentiveness without requiringhuman interaction.

It can be understood from the discussion above that other dataindicative of attentiveness may be used instead of theattentiveness-labelled response data as the target for the neuralnetwork. For example, attentiveness data used for model training mayconsist or comprise physiological data and media attentiveness saliencydata.

FIG. 5 is a schematic screen shot of an example annotation tool 500 thatmay be used in embodiments of the invention. The annotation tool 500 isa graphical user interface that is displayable on the display screen ofa computing device. It comprises a split-screen playback panel which isarranged to play the images of the user in a first portion 502 thereofand to display the media content being watched in a second portion 504thereof. It can be understood from the discussion above that othercollected information can also be displayed, e.g. relating tophysiological data, etc. For example, information indicative of anintensity of a physiological response may be provided in a physiologicaldata display panel 512, and information indicative of variation in mediaattentiveness saliency may be provided in a saliency data display panel514. In another example, a previously trained attentiveness model may beused to automatically detect attentiveness and display data in thelabelling tool, e.g. as a prompt to aid the annotation process.

Playback split-screen playback panel is controllable, e.g. via a controlinterface panel 506.

In this example, attentiveness scores are applied in a score applicatorportion 508. This example allows application of one of three attentionlevels: high, medium or low, to portions of the response. A timeline forthe response if provided for each attention level. A user can score aparticular portion of the response in one of the attention levels byselecting or highlighting the timeline in that attention level for theappropriate duration. Where no attention level is selected for a portionof the response, it can be assumed that there is no attention, i.e. theuser was absent or otherwise totally disengaged.

The annotator tool 500 further includes a summary panel 510 which liststhe timeframes within the response that have been tagged with anattention level. The annotator may edit the summary panel 510 to affectthe appearance of the score applicator portion 508.

It may be understood that the annotator tool depicted in FIG. 5represents one of many ways in which tags may be applied to responsedata. The invention need not be limited to the arrangement shown in FIG.5.

FIG. 6 is a flow diagram of a data analysis method 600 that utilises theattentiveness-labelled response data discussed above. The method beginswith a step 602 of establishing multiple attentiveness proxy parameters.The proxy parameter represent features within the behavioural data thathave been observed to correlate with attentiveness. These features maybe established using data entered in the reasons field by eachannotator. Example features may include head-pose, gaze direction,heightened emotion levels, blinks, facial expressivity, body gestures,heart rate, activities like eating or drinking, speaking, etc.

The method continues with a step 604 of generating an attentivenessproxy sub-model for each of the features identified in step 602. Unlikethe attentiveness model discussed with reference to FIG. 4, eachattentiveness proxy sub-model is established using a training set thatcomprises a subset of the attentiveness-labelled response data relatingto its respective feature. Each attentiveness proxy sub-model isconfigured to supply an attentiveness score for input datarepresentative of its respective feature.

The method continues with a step 606 of generating an attentivenessmodel that comprises an ensemble model that uses outputs from theplurality of attentiveness proxy sub-models as inputs. The ensemblemodel may be trained using the attentiveness data to apply appropriateweighting to the attentiveness proxy sub-model outputs.

The ensemble model can be used with new response data (i.e. responsedata without annotation) to supply an output indicative ofattentiveness, together with a confidence score for that output. Forexample, the output indicative of attentiveness may be obtained from theplurality of attentiveness proxy sub-models, e.g. as an attentivenessscore for different portions of the new response data obtaining byaggregating or averaging or otherwise processing the outputs from theattentiveness proxy sub-models. The attentiveness score may be a binaryindication, i.e. indicating the presence or absence of user attention.In some examples, the proxy sub-models may provide only a positive ornegative indication, i.e. only one of “attentive” or “not-attentive”.The confidence score may be a numeric value that quantifies theconfidence in the attentiveness score.

1. A computer-implemented method of determining user attentivenessduring media content consumption, the method comprising: obtaining, at acollection server, response data from a client device, wherein theresponse data is collected for a user consuming media content on theclient device, and wherein the response data comprises a data streamrepresentative of variation over time of the user's behaviour whilstconsuming the media content; associating, at the collection server, thedata stream with the media content; displaying, at an annotation device,a dynamic representation of the response data concurrently with themedia content to which it is associated; receiving, at the annotationdevice, attentiveness data from an annotator, wherein the attentivenessdata is an input score indicative of user attentiveness based on thedynamic representation; and associating, at the annotation device, theattentiveness data with events in the data stream or media content togenerate attentiveness-labelled response data.
 2. The method of claim 1,wherein the data stream comprises information indicative of timeevolution of one or more response data parameters.
 3. The method ofclaim 1, wherein the response data comprises emotional state dataobtained from facial image data of the user that is collected duringconsumption of the media content.
 4. The method of claim 1, whereinassociating the data stream with the media content comprisessynchronising the response data with the media content.
 5. The method ofclaim 4, wherein the response data is time stamped, and whereinsynchronising the response data with the media content comprisingmatching time stamp data with media playback status data.
 6. The methodof claim 1, wherein the data stream further comprises any of: mediacontrol data, media tag data, media attentiveness saliency data, andmedia playback status data.
 7. The method of claim 1, wherein generatingthe attentiveness-labelled response data comprises adding anattentiveness label parameter to the response data.
 8. The method ofclaim 1, wherein the attentiveness data is selected from any one of: abinary indicator, a plurality of preset levels, and a sliding scale. 9.The method of claim 1, wherein the response data comprises emotionalstate data, and wherein the method further comprises deriving asignificance score or weighting for the emotion state data based on thereceived attentiveness data.
 10. The method of claim 1 includingcontrolling the concurrently displayed dynamic representation of theresponse data and the media content to which it is associated.
 11. Themethod of claim 1 further comprising: receiving, at an analysis server,attentiveness data relating to the dynamic representation from aplurality of annotators; generating, by the analysis server, combinedattentiveness data for the dynamic representation, the combinedattentiveness data comprising an attentiveness parameter that isindicative of level of positive correlation between the attentivenessdata from the plurality of annotators.
 12. The method of claim 11,wherein the input score for the attentiveness data is a binary indicatorand the attentiveness parameter is a continuous variable.
 13. The methodof claim 1 further comprising: storing, in a data repository,attentiveness-labelled response data from multiple users; extracting,from the data repository by an analysis server, anattentiveness-labelled response data training set; establishing, at theanalysis server, an objective for a machine learning algorithm; andgenerating, using the machine learning algorithm, an attentiveness modelfrom the attentiveness-labelled response data training set.
 14. Themethod of claim 13 further comprising: obtaining, at the collectionserver, new response data from a client device, wherein the responsedata is collected for another user consuming media content on the clientdevice, and wherein the response data comprises a data streamrepresentative of variation over time of the user's behaviour whilstconsuming the media content; inputting the new response data to theattentiveness model to obtain an attentiveness score for one or moreportions of the new response data.
 15. The method of claim 13, whereinextracting the attentiveness-labelled response data training setcomprises applying a filter to the attentiveness-labelled response datain the repository.
 16. The method of claim 13, wherein theattentiveness-labelled response data in the training set comprises mediatag data indicative of subject matter in the media content beingconsumed, and wherein the method further comprises: obtaining new mediatag data for another piece of media content, and inputting the new mediatag data to the attentiveness model to obtain an attractiveness scorefor one or more portions of the piece of media content.
 17. The methodof claim 1 further comprising: storing, in a data repository,attentiveness-labelled response data from multiple users; generating,using the attentiveness-labelled response data, a plurality ofattentiveness proxy sub-models, wherein each of the attentiveness proxysub-models is trained based on a respective attentiveness proxyparameter to supply an output indicative of attentiveness from theresponse data; and establishing an ensemble model that uses outputs fromthe plurality of attentiveness proxy sub-models to supply anattentiveness confidence score.
 18. The method of claim 17 furthercomprising: obtaining, at the collection server, new response data froma client device, wherein the response data is collected for another userconsuming media content on the client device, and wherein the responsedata comprises a data stream representative of variation over time ofthe user's behaviour whilst consuming the media content; inputting thenew response data to the plurality of attentiveness proxy sub-models toobtain an attentiveness score for one or more portions of the newresponse data; and inputting the outputs of the plurality ofattentiveness proxy sub-models to the ensemble model to obtain anattentiveness confidence score associated with the attentiveness score.19. A computer-implemented method of determining user attentivenessduring media content consumption, the method comprising: obtaining, at acollection server, response data from a client device, wherein theresponse data is collected for a user consuming media content on theclient device, and wherein the response data comprises a data streamrepresentative of variation over time of the user's behaviour whilstconsuming the media content; associating, at the collection server, thedata stream with the media content; displaying, at a plurality ofannotation devices, a dynamic representation of the response data;receiving, at the plurality of annotation devices, attentiveness datafrom an annotator, wherein the attentiveness data is an input scoreindicative of user attentiveness based on the dynamic representation;receiving, at an analysis server from the plurality of annotationdevices, attentiveness data relating to the dynamic representation froma plurality of annotators; generating, by the analysis server, combinedattentiveness data for the dynamic representation, the combinedattentiveness data comprising an attentiveness parameter that isindicative of level of positive correlation between the attentivenessdata from the plurality of annotators; and associating, at the analysisserver, the combined attentiveness data with events in the data streamor media content to generate attentiveness-labelled response data.
 20. Asystem for determining user attentiveness during media contentconsumption, the system comprising: a collection server communicativelycoupled via a network to a plurality of client devices, the collectionserver being configured to: obtain response data from the plurality ofclient devices, wherein the response data is collected for a userconsuming media content on the client device, and wherein the responsedata comprises a data stream representative of variation over time ofthe user's behaviour whilst consuming the media content; and associatethe data stream to the media content; and an annotation devicecommunicatively coupled to the collection server, the annotation devicebeing configured to: display a dynamic representation of the responsedata concurrently with the media content to which it is associated;receive attentiveness data that comprises an input score indicative ofuser attentiveness based on the dynamic representation; and associatethe attentiveness data with events in the data stream or media contentto generate attentiveness-labelled response data.